mlsanityData to 7 Oct 2026

Learning Deep CNN Denoiser Prior for Image Restoration

arXiv 1704.03264 ↗First posted 11 Apr 2017cs.CVcited by 407

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Cited by 37 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance; in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.

Key facts. Cited by 407 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Apr 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 86; 2022: 81; 2023: 90; 2024: 64; 2025: 49; 2026: 37.

407 papers.

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  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  2. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,618
  3. 2021Multi-Stage Progressive Image Restorationcited by 813
  4. 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 524
  5. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  6. 2021GAN Inversion: A Surveycited by 366
  7. 2024MambaIR: A Simple Baseline for Image Restoration with State-Space Modelcited by 349
  8. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
  9. 2024Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wildcited by 281
  10. 2022Convergence of denoising diffusion models under the manifold hypothesiscited by 237
  11. 2021Learning to Optimize: A Primer and A Benchmarkcited by 206
  12. 2021SNIPS: Solving Noisy Inverse Problems Stochasticallycited by 191
38 more
  1. 2023Image Restoration with Mean-Reverting Stochastic Differential Equationscited by 182
  2. 2024InstructIR: High-Quality Image Restoration Following Human Instructionscited by 139
  3. 2021Deep Equilibrium Architectures for Inverse Problems in Imagingcited by 126
  4. 20223D Common Corruptions and Data Augmentationcited by 120
  5. 2022Deep Generalized Unfolding Networks for Image Restorationcited by 119
  6. 2023PromptIR: Prompting for All-in-One Blind Image Restorationcited by 113
  7. 2024AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulationcited by 108
  8. 2021Gradient Step Denoiser for convergent Plug-and-Playcited by 107
  9. 2021Bayesian imaging using Plug & Play priors: when Langevin meets Tweediecited by 96
  10. 2022Deep Image Deblurring: A Surveycited by 94
  11. 2021Blind Image Super-Resolution: A Survey and Beyondcited by 93
  12. 2024Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priorscited by 93
  13. 2024MambaIRv2: Attentive State Space Restorationcited by 90
  14. 2023Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Modelscited by 86
  15. 2022Accurate Image Restoration with Attention Retractable Transformercited by 85
  16. 2022Tutorial on amortized optimizationcited by 83
  17. 2022Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularizationcited by 79
  18. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 72
  19. 2024Complexity Experts are Task-Discriminative Learners for Any Image Restorationcited by 72
  20. 2021Discrete Cosine Transform Network for Guided Depth Map Super-Resolutioncited by 71
  21. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
  22. 2023Training-free Linear Image Inverses via Flowscited by 70
  23. 2021On Efficient Transformer-Based Image Pre-training for Low-Level Visioncited by 64
  24. 2023HAT: Hybrid Attention Transformer for Image Restorationcited by 64
  25. 2023Omni Aggregation Networks for Lightweight Image Super-Resolutioncited by 62
  26. 2021Improving Image Restoration by Revisiting Global Information Aggregationcited by 60
  27. 2024A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trendscited by 58
  28. 2021UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolutioncited by 56
  29. 2023A Comparative Study of Image Restoration Networks for General Backbone Network Designcited by 56
  30. 2022TAPE: Task-Agnostic Prior Embedding for Image Restorationcited by 54
  31. 2021Dynamic Attentive Graph Learning for Image Restorationcited by 48
  32. 2021Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fieldscited by 48
  33. 2023WaveDM: Wavelet-Based Diffusion Models for Image Restorationcited by 46
  34. 2023Neural Degradation Representation Learning for All-In-One Image Restorationcited by 46
  35. 2022Equivariant Hypergraph Diffusion Neural Operatorscited by 45
  36. 2021Deep Gradient Projection Networks for Pan-sharpeningcited by 44
  37. 2022ShuffleMixer: An Efficient ConvNet for Image Super-Resolutioncited by 44
  38. 2023Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidancecited by 43

50 most cited of 407 All 407 (JSON)